[R] Difficulties in trying to do a mixed effects model using the lmer function
gunter.berton at gene.com
Wed Oct 3 19:08:25 CEST 2012
Please post this on the r-sig-mixed-models list, not here. You are
waayyy more likely to get useful help there.
Also this is primarily a statistics, not an R issue. You may wish to
consider consulting with your local statistician to help you
understand the statistics.
On Wed, Oct 3, 2012 at 8:54 AM, Riedel Judith
<judith.riedel at ipw.agrl.ethz.ch> wrote:
> Dear people of the help list
> I am drying to analyze my data using the 'lmer' function and I keep having problems.
> This is the model:
>> fm1<-lmer(dbh~spec+scheme+(1|Plot),data=d, REML=FALSE).
> I analyse tree size (dbh) of 3 different species (spec) and 3 planting schemes (scheme). I have 5 plots, which I hope to model as a random factor. (However, the subsequent output is based on some simplified dummy data, which is based on only two plots and ha only few observations).
> No I do:
> and I get some output, which I don't understand. Looks like this:
> Analysis of Variance Table
> Df Sum Sq Mean Sq F value
> spec 2 6.098 3.0490 0.6142
> scheme 2 13.161 6.5803 1.3255
> The problems I have are:
> (1) How can I get the P-values?
> (2) How can I get the overall model statistic?
> Than I do:
> and get:
> Linear mixed model fit by maximum likelihood
> Formula: dbh ~ spec + scheme + (1 | Plot)
> Data: d
> AIC BIC logLik deviance REMLdev
> 147.2 157 -66.6 133.2 125.8
> Random effects:
> Groups Name Variance Std.Dev.
> Plot (Intercept) 0.0000 0.0000
> Residual 4.9644 2.2281
> Number of obs: 30, groups: Plot, 2
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 6.9074 0.9424 7.329
> specCED 0.3859 1.0265 0.376
> specTAB 0.8585 0.9828 0.874
> schemeMON 0.6572 0.9554 0.688
> schemePRO -1.0344 1.1259 -0.919
> Correlation of Fixed Effects:
> (Intr) spcCED spcTAB schMON
> specCED -0.537
> specTAB -0.529 0.500
> schemeMON -0.588 0.002 -0.072
> schemePRO -0.565 0.064 0.063 0.510
> What is this? What does it tell me?
> The statistics help advised me to do a second model, like this:
> But why would I compare the two models?
> What I get is:
> Data: d
> fm2: dbh ~ scheme + (1 | Plot)
> fm1: dbh ~ spec + scheme + (1 | Plot)
> Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
> fm2 5 143.96 150.97 -66.982
> fm1 7 147.21 157.01 -66.602 0.7584 2 0.6844
> What does this mean? Why Chi?
> Finally I would like to do some LSD post hoc tests, but I have no idea how to do it.
> In the end I would like to be able to report something like: 'DBH differed significantly between, species, planting schemes, and plots (Fx,xx = X; P = X). DBH of species 1 was significantly larger than DBH of species 2 (LSD post hoc test, P = X)'.
> I greatly appreciate any suggestions! Thank You a lot for Your help!
> Kind regards,
> PS. the complete output is attached.
> Judith Riedel
> ETH Zurich
> Institute of Agricultural Sciences
> Applied Entomology
> Schmelzbergstrasse 9/LFO
> 8092 Zurich
> Tel: ++41 44 632 3923
> Fax: ++41 44 632 1171
> judith.riedel at ipw.agrl.ethz.ch<mailto:judith.riedel at ipw.agrl.ethz.ch>
> R-help at r-project.org mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
Genentech Nonclinical Biostatistics
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